Catecholamines, not acetylcholine, alter cortical and perceptual dynamics in line with increased excitation-inhibition ratio

The ratio between excitatory and inhibitory neurons (E/I ratio) is vital for cortical circuit dynamics, computation, and behavior. This ratio may be under the dynamic control of neuromodulatory systems, which are in turn implicated in several neuropsychiatric disorders. In particular, the catecholaminergic (dopaminergic and noradrenergic) and cholinergic systems have highly specific effects on excitatory and inhibitory cortical neurons, which might translate into changes in the local net E/I ratio. Here, we assessed and compared their net effects on net E/I ratio in human cortex, through an integrated application of computational modeling, placebo-controlled pharmacological intervention, magnetoencephalographic recordings of cortical activity dynamics, and perceptual psychophysics. We found that catecholamines, but not acetylcholine, altered both the temporal structure of intrinsic activity fluctuations in visual and parietal cortex, and the volatility of perceptual inference based on ambiguous visual input. Both effects indicate that catecholamines increase the net E/I ratio in visual and parietal cortex.

[1]  H. Adesnik Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice , 2017, Neuron.

[2]  Y. Dan,et al.  Cholinergic shaping of neural correlations , 2017, Proceedings of the National Academy of Sciences.

[3]  Niels A. Kloosterman,et al.  Dynamic modulation of decision biases by brainstem arousal systems , 2017, eLife.

[4]  T. Fuchs,et al.  Disinhibition of somatostatin-positive GABAergic interneurons results in an anxiolytic and antidepressant-like brain state , 2016, Molecular Psychiatry.

[5]  Amélie J. Reynaud,et al.  Boosting Norepinephrine Transmission Triggers Flexible Reconfiguration of Brain Networks at Rest , 2016, Cerebral cortex.

[6]  Peter R Murphy,et al.  Catecholaminergic Neuromodulation Shapes Intrinsic MRI Functional Connectivity in the Human Brain , 2016, The Journal of Neuroscience.

[7]  Mario Treviño,et al.  Layer- and area-specific actions of norepinephrine on cortical synaptic transmission , 2016, Brain Research.

[8]  R. Mooney,et al.  The Basal Forebrain and Motor Cortex Provide Convergent yet Distinct Movement-Related Inputs to the Auditory Cortex , 2016, Neuron.

[9]  Christian K. Machens,et al.  Efficient codes and balanced networks , 2016, Nature Neuroscience.

[10]  Oren Shriki,et al.  Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network , 2016, PLoS Comput. Biol..

[11]  Oren Shriki,et al.  Near-Critical Dynamics in Stimulus-Evoked Activity of the Human Brain and Its Relation to Spontaneous Resting-State Activity , 2015, The Journal of Neuroscience.

[12]  Jessica A. Cardin,et al.  Waking State: Rapid Variations Modulate Neural and Behavioral Responses , 2015, Neuron.

[13]  S. Nelson,et al.  Excitatory/Inhibitory Balance and Circuit Homeostasis in Autism Spectrum Disorders , 2015, Neuron.

[14]  Gustavo Deco,et al.  Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling , 2015, PLoS Comput. Biol..

[15]  Woodrow L. Shew,et al.  Adaptation to sensory input tunes visual cortex to criticality , 2015, Nature Physics.

[16]  Robert C. Froemke,et al.  Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex , 2015, Nature Neuroscience.

[17]  R. Froemke Plasticity of cortical excitatory-inhibitory balance. , 2015, Annual review of neuroscience.

[18]  J. Palva,et al.  Relationship of Fast- and Slow-Timescale Neuronal Dynamics in Human MEG and SEEG , 2015, The Journal of Neuroscience.

[19]  Mriganka Sur,et al.  An acetylcholine-activated microcircuit drives temporal dynamics of cortical activity , 2015, Nature Neuroscience.

[20]  H. Kennedy,et al.  A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex , 2015, Neuron.

[21]  Woodrow L. Shew,et al.  Cascades and Cognitive State: Focused Attention Incurs Subcritical Dynamics , 2015, The Journal of Neuroscience.

[22]  C. Petersen,et al.  Cholinergic signals in mouse barrel cortex during active whisker sensing. , 2014, Cell reports.

[23]  David J. Freedman,et al.  A hierarchy of intrinsic timescales across primate cortex , 2014, Nature Neuroscience.

[24]  Thilo Gross,et al.  Self-organized criticality as a fundamental property of neural systems , 2014, Front. Syst. Neurosci..

[25]  Pierre Yger,et al.  Brian 2: neural simulations on a variety of computational hardware , 2014, BMC Neuroscience.

[26]  Li I. Zhang,et al.  Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex , 2014, Nature Neuroscience.

[27]  Christian K. Machens,et al.  Variability in neural activity and behavior , 2014, Current Opinion in Neurobiology.

[28]  M. Stryker,et al.  A Cortical Circuit for Gain Control by Behavioral State , 2014, Cell.

[29]  Richard E. Carson,et al.  Clinical doses of atomoxetine significantly occupy both norepinephrine and serotonin transports: Implications on treatment of depression and ADHD , 2014, NeuroImage.

[30]  G. Fishell,et al.  Interneuron cell types are fit to function , 2014, Nature.

[31]  Michael J. Goard,et al.  Fast Modulation of Visual Perception by Basal Forebrain Cholinergic Neurons , 2013, Nature Neuroscience.

[32]  Andrew J. Parker,et al.  A Causal Role for V5/MT Neurons Coding Motion-Disparity Conjunctions in Resolving Perceptual Ambiguity , 2013, Current Biology.

[33]  P. Golshani,et al.  Cellular mechanisms of brain-state-dependent gain modulation in visual cortex , 2013, Nature Neuroscience.

[34]  M. Siegel,et al.  Dissociating neuronal gamma-band activity from cranial and ocular muscle activity in EEG , 2013, Front. Hum. Neurosci..

[35]  Tomas Knapen,et al.  GABA Shapes the Dynamics of Bistable Perception , 2013, Current Biology.

[36]  Viola Priesemann,et al.  Neuronal Avalanches Differ from Wakefulness to Deep Sleep – Evidence from Intracranial Depth Recordings in Humans , 2013, PLoS Comput. Biol..

[37]  K. Linkenkaer-Hansen,et al.  Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws , 2013, Proceedings of the National Academy of Sciences.

[38]  Dov Sagi,et al.  Retinotopic Patterns of Correlated Fluctuations in Visual Cortex Reflect the Dynamics of Spontaneous Perceptual Suppression , 2013, The Journal of Neuroscience.

[39]  M. Carandini,et al.  Inhibition dominates sensory responses in awake cortex , 2012, Nature.

[40]  Vadim V. Nikulin,et al.  Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations , 2012, Front. Physio..

[41]  D. Heeger,et al.  Slow Cortical Dynamics and the Accumulation of Information over Long Timescales , 2012, Neuron.

[42]  K. Linkenkaer-Hansen,et al.  Critical-State Dynamics of Avalanches and Oscillations Jointly Emerge from Balanced Excitation/Inhibition in Neuronal Networks , 2012, The Journal of Neuroscience.

[43]  E. Wagenmakers,et al.  A default Bayesian hypothesis test for correlations and partial correlations , 2012, Psychonomic bulletin & review.

[44]  J. Lisman Excitation, inhibition, local oscillations, or large-scale loops: what causes the symptoms of schizophrenia? , 2012, Current Opinion in Neurobiology.

[45]  R. Dolan,et al.  Cholinergic Enhancement of Visual Attention and Neural Oscillations in the Human Brain , 2012, Current Biology.

[46]  M. Scanziani,et al.  How Inhibition Shapes Cortical Activity , 2011, Neuron.

[47]  Rolando J. Biscay-Lirio,et al.  Assessing interactions in the brain with exact low-resolution electromagnetic tomography , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[48]  Biyu J. He Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task , 2011, The Journal of Neuroscience.

[49]  Lief E. Fenno,et al.  Neocortical excitation/inhibition balance in information processing and social dysfunction , 2011, Nature.

[50]  K. Harris,et al.  Cortical state and attention , 2011, Nature Reviews Neuroscience.

[51]  D. Knill,et al.  Bayesian sampling in visual perception , 2011, Proceedings of the National Academy of Sciences.

[52]  M. Siegel,et al.  A framework for local cortical oscillation patterns , 2011, Trends in Cognitive Sciences.

[53]  C. Schroeder,et al.  Neuronal Mechanisms and Attentional Modulation of Corticothalamic Alpha Oscillations , 2011, The Journal of Neuroscience.

[54]  T. Moore,et al.  CONTROL OF VISUAL CORTICAL SIGNALS BY PREFRONTAL DOPAMINE , 2011, Nature.

[55]  J. Poulet,et al.  Synaptic Mechanisms Underlying Sparse Coding of Active Touch , 2011, Neuron.

[56]  A. E. Eiben,et al.  Parameter tuning for configuring and analyzing evolutionary algorithms , 2011, Swarm Evol. Comput..

[57]  D. Louis Collins,et al.  Unbiased average age-appropriate atlases for pediatric studies , 2011, NeuroImage.

[58]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[59]  Woodrow L. Shew,et al.  Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches , 2010, The Journal of Neuroscience.

[60]  D. Chialvo Emergent complex neural dynamics , 2010, 1010.2530.

[61]  Biyu J. He,et al.  The Temporal Structures and Functional Significance of Scale-free Brain Activity , 2010, Neuron.

[62]  Takeo Watanabe,et al.  Perceptual learning rules based on reinforcers and attention , 2010, Trends in Cognitive Sciences.

[63]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[64]  Jeffrey G. Ojemann,et al.  Power-Law Scaling in the Brain Surface Electric Potential , 2009, PLoS Comput. Biol..

[65]  M. Frank,et al.  Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation. , 2009, Nature neuroscience.

[66]  T. Robbins,et al.  The neuropsychopharmacology of fronto-executive function: monoaminergic modulation. , 2009, Annual review of neuroscience.

[67]  Woodrow L. Shew,et al.  Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality , 2009, The Journal of Neuroscience.

[68]  Jeffrey N. Rouder,et al.  Bayesian t tests for accepting and rejecting the null hypothesis , 2009, Psychonomic bulletin & review.

[69]  Philip Holmes,et al.  Optimality and Robustness of a Biophysical Decision-Making Model under Norepinephrine Modulation , 2009, The Journal of Neuroscience.

[70]  S. Sara The locus coeruleus and noradrenergic modulation of cognition , 2009, Nature Reviews Neuroscience.

[71]  Arjen van Ooyen,et al.  Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease , 2009, Proceedings of the National Academy of Sciences.

[72]  R. Romo,et al.  The role of fluctuations in perception , 2008, Trends in Neurosciences.

[73]  Xiao-Jing Wang Decision Making in Recurrent Neuronal Circuits , 2008, Neuron.

[74]  C. Berridge Noradrenergic modulation of arousal , 2008, Brain Research Reviews.

[75]  John M Beggs,et al.  The criticality hypothesis: how local cortical networks might optimize information processing , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[76]  Arjen van Ooyen,et al.  Genetic Contributions to Long-Range Temporal Correlations in Ongoing Oscillations , 2007, The Journal of Neuroscience.

[77]  C. Schreiner,et al.  A synaptic memory trace for cortical receptive field plasticity , 2007, Nature.

[78]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[79]  J. Rinzel,et al.  Noise-induced alternations in an attractor network model of perceptual bistability. , 2007, Journal of neurophysiology.

[80]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[81]  R. van Ee,et al.  Percept-choice sequences driven by interrupted ambiguous stimuli: a low-level neural model. , 2007, Journal of vision.

[82]  A. Arnsten,et al.  Adrenergic pharmacology and cognition: focus on the prefrontal cortex. , 2007, Pharmacology & therapeutics.

[83]  R. van Ee,et al.  Visual Cortex Allows Prediction of Perceptual States during Ambiguous Structure-From-Motion , 2007, The Journal of Neuroscience.

[84]  O. Kinouchi,et al.  Optimal dynamical range of excitable networks at criticality , 2006, q-bio/0601037.

[85]  Jonathan D. Cohen,et al.  An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. , 2005, Annual review of neuroscience.

[86]  Felix Hasler,et al.  Modulating the Rate and Rhythmicity of Perceptual Rivalry Alternations with the Mixed 5-HT2A and 5-HT1A Agonist Psilocybin , 2005, Neuropsychopharmacology.

[87]  Angela J. Yu,et al.  Uncertainty, Neuromodulation, and Attention , 2005, Neuron.

[88]  John-Michael Sauer,et al.  Clinical Pharmacokinetics of Atomoxetine , 2005, Clinical pharmacokinetics.

[89]  P. Glimcher Indeterminacy in brain and behavior. , 2005, Annual review of psychology.

[90]  K. Linkenkaer-Hansen,et al.  Prestimulus Oscillations Enhance Psychophysical Performance in Humans , 2004, The Journal of Neuroscience.

[91]  John M. Beggs,et al.  Neuronal Avalanches in Neocortical Circuits , 2003, The Journal of Neuroscience.

[92]  G. Nolte The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. , 2003, Physics in medicine and biology.

[93]  Kenneth D Miller,et al.  Multiplicative Gain Changes Are Induced by Excitation or Inhibition Alone , 2003, The Journal of Neuroscience.

[94]  Ralf Engbert,et al.  Microsaccades uncover the orientation of covert attention , 2003, Vision Research.

[95]  Harvey A Swadlow,et al.  Thalamocortical control of feed-forward inhibition in awake somatosensory 'barrel' cortex. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[96]  Xiao-Jing Wang,et al.  Probabilistic Decision Making by Slow Reverberation in Cortical Circuits , 2002, Neuron.

[97]  K. Perry,et al.  Atomoxetine Increases Extracellular Levels of Norepinephrine and Dopamine in Prefrontal Cortex of Rat: A Potential Mechanism for Efficacy in Attention Deficit/Hyperactivity Disorder , 2002, Neuropsychopharmacology.

[98]  A. Parker,et al.  Neuronal activity and its links with the perception of multi-stable figures. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[99]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[100]  Xiao-Jing Wang Synaptic reverberation underlying mnemonic persistent activity , 2001, Trends in Neurosciences.

[101]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[102]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[103]  P. Mitra,et al.  Analysis of dynamic brain imaging data. , 1998, Biophysical journal.

[104]  L. Friedhoff,et al.  Pharmacokinetic and pharmacodynamic profile of donepezil HCl following evening administration. , 1998, British journal of clinical pharmacology.

[105]  W. Newsome,et al.  The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.

[106]  H. Sompolinsky,et al.  Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.

[107]  Per Bak,et al.  The Discovery of Self-Organized Criticality , 1996 .

[108]  C. Pennartz The ascending neuromodulatory systems in learning by reinforcement: comparing computational conjectures with experimental findings , 1995, Brain Research Reviews.

[109]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[110]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[111]  J D Cohen,et al.  A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior. , 1990, Science.

[112]  G Sperling,et al.  How to study the kinetic depth effect experimentally. , 1990, Journal of experimental psychology. Human perception and performance.

[113]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[114]  S. Foote,et al.  Noradrenergic and serotoninergic innervation of cortical, thalamic, and tectal visual structures in old and new world monkeys , 1986, The Journal of comparative neurology.

[115]  H. Wallach,et al.  The kinetic depth effect. , 1953, Journal of experimental psychology.